Effects of beta-alanine supplementation and interval training on physiological determinants of severe exercise performance.

INTRODUCTION: We aimed to manipulate physiological determinants of severe exercise performance. We hypothesized that (1) beta-alanine supplementation would increase intramuscular carnosine and buffering capacity and dampen acidosis during severe cycling, (2) that high-intensity interval training (HIT) would enhance aerobic energy contribution during severe cycling, and (3) that HIT preceded by beta-alanine supplementation would have greater benefits. METHODS: Sixteen active men performed incremental cycling tests and 90-s severe (110 % peak power) cycling tests at three time points: before and after oral supplementation with either beta-alanine or placebo, and after an 11-days HIT block (9 sessions, 4 × 4 min), which followed supplementation. Carnosine was assessed via MR spectroscopy. Energy contribution during 90-s severe cycling was estimated from the O2 deficit. Biopsies from m. vastus lateralis were taken before and after the test. RESULTS: Beta-alanine increased leg muscle carnosine (32 ± 13 %, d = 3.1). Buffering capacity and incremental cycling were unaffected, but during 90-s severe cycling, beta-alanine increased aerobic energy contribution (1.4 ± 1.3 %, d = 0.5), concurrent with reduced O2 deficit (-5.0 ± 5.0 %, d = 0.6) and muscle lactate accumulation (-23 ± 30 %, d = 0.9), while having no effect on pH. Beta-alanine also enhanced motivation and perceived state during the HIT block. There were no between-group differences in adaptations to the training block, namely increased buffering capacity (+7.9 ± 11.9 %, p = 0.04, d = 0.6, n = 14) and glycogen storage (+30 ± 47 %, p = 0.04, d = 0.5, n = 16). CONCLUSIONS: Beta-alanine did not affect buffering considerably, but has beneficial effects on severe exercise metabolism as well as psychological parameters during intense training phases.

On the use of Cramér-Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments.

Localized Magnetic Resonance Spectroscopy (MRS) is in widespread use for clinical brain research. Standard acquisition sequences to obtain one-dimensional spectra suffer from substantial overlap of spectral contributions from many metabolites. Therefore, specially tuned editing sequences or two-dimensional acquisition schemes are applied to extend the information content. Tuning specific acquisition parameters allows to make the sequences more efficient or more specific for certain target metabolites. Cramér-Rao bounds have been used in other fields for optimization of experiments and are now shown to be very useful as design criteria for localized MRS sequence optimization. The principle is illustrated for one- and two-dimensional MRS, in particular the 2D separation experiment, where the usual restriction to equidistant echo time spacings and equal acquisition times per echo time can be abolished. Particular emphasis is placed on optimizing experiments for quantification of GABA and glutamate. The basic principles are verified by Monte Carlo simulations and in vivo for repeated acquisitions of generalized two-dimensional separation brain spectra obtained from healthy subjects and expanded by bootstrapping for better definition of the quantification uncertainties.

Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets.

OBJECT: To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T(1) and T(2) differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets. MATERIALS AND METHODS: Series of localized inversion-recovery (IR) and 2DJ separation spectra of the brain were recorded at 3T. The MMBL was determined by three 2D evaluation methods based on (1) IR spectra only, (2) 2DJ spectra only, (3) both IR and 2DJ spectra (2DJ-IR). Their performance was compared using synthetic spectra and based on variability and reproducibility as obtained in vivo from 12 subjects in 20 examinations. RESULTS: All methods performed well for synthetic data. In vivo, 2DJ-only yielded larger variations than the other methods. IR-only and 2DJ-IR yielded similar performance. FiTAID is illustrated with further applications where linear-combination model fitting of interrelated arrays of spectra is advantageous. CONCLUSION: 2D-Fitting offers the possibility to determine the MMBL based on a range of complementary experimental spectra not relying on smoothness criteria or global assumptions on T(1). Since 2DJ-IR includes information from spectra with different inversion and echo times, it is expected to be more robust in cases with more variable data quality and overlap with lipid resonances.